About
Chris is a Senior Enterprise Fellow as part of the IT Innovation Centre within the School of Electronics and Computer Science. He is also part of the Digital Health and Biomedical Engineering Research Group and holds honorary positions within the NHS, including University Hospitals Southampton Foundation Trust.
Chris has significant experience in translating AI research into real-world impact in industry and the NHS. He is currently PI on PROCED-DST which considers how machine learning could support clinicians manage complex discharge and is CO-I on a number of a number of large-scale interdisciplinary research programmes at the boundary of technology and society (e.g., healthcare, finance).
Chris' main area of expertise lies in AI and transparency, aiming to understand how best to communicate and involve everyone in the design of machine learning. Chris' technological interests include data drift, explainable machine learning (XAI), knowledge graphs and large language models.
Chris’ original background is Astrophysics (St Andrews & Flatiron Institute) but switched to Digital Health in 2021. More in the biography.
Research
Research groups
Research interests
- Artificial Intelligence for Health and Wellbeing
- Explainable Machine Learning
- Human Centred Interactive Systems
- Chronic Disease Management
Current research
Chris' current research focusses on the following areas:
- Chronic Disease Management: This research involves the application of AI and machine learning to understand if it can help reduce the burden of managing a chronic condition. A variety of technology exists (self-management apps, sensing devices), however, engagement is critical for its success and the ability to train AI from this data. Ongoing projects tackling type-one diabetes and chronic obstructive pulmonary disease have directly included people with the conditions, family members, and clinical care teams to ensure needs are met.
- Hospital Trajectories and Outcomes: This research area involves using machine learning techniques to develop decision support tools for clinicians. Utilising mixed data including Electronic Health Records and imaging, ongoing projects aim to identify patients at high risk and in-need of further clinical review.
Chris' previous research in Astrophysics considered the relationship between angular momentum in galaxies and the cosmic web:
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- Decoupling the rotation of stars and gas – I. The relationship with morphology and halo spin
- Decoupling the rotation of stars and gas – II. The link between black hole activity and simulated IFU kinematics in IllustrisTNG
- SDSS-IV MaNGA: signatures of halo assembly in kinematically misaligned galaxies
- SDSS-IV MaNGA: 3D spin alignment of spiral and S0 galaxies
Research projects
Active projects
Completed projects
Publications
External roles and responsibilities
Biography
Chris has significant experience in the application of statistical and machine learning techniques in Healthcare, Finance, and Astrophysics. Working within large interdisciplinary teams, he is a specialist in applied research and translating this into real-world impact. With a particular focus on Digital Health, Chris also tackles problems around acceptance and adoption of technology; including Artificial Intelligence (AI).
A key factor of acceptance is transparency and understanding. As a result Chris has significant experience in explainable AI (XAI), which aims to highlight the important factors in why a decision or prediction has been made by a model.
Chris is also involved in communicating AI to industry partners, clinicians, and the public. Working directly with psychologists and human-computer interaction experts, his work aims to include all stakeholders into the design process through co-design.